Primer on Logistic Regression for Emergency Care Researchers

Logistic regression plays a fundamental role in the production of decision rules, risk assessment, and in establishing cause and effect relationships. This primer is aimed at novice researchers with minimal statistical expertise.

Introduce the logit equation and provide a hands-on example to facilitate understanding of its benefits and limitations.

This primer reviews the mathematical basis of a logit equation by comparing and contrasting it with the simple straight-line (linear) equation. After gaining an understanding of the meaning of beta coefficients, readers are encouraged to download a free statistical program and database to produce a logistic regression analysis. Using this example, the narrative then discusses commonly used methods to describe model fitness, including the C-statistic, chi square, Akaike and Bayesian Information Criteria, McFadden's pseudo R², and the Hosmer-Lemeshow test. The authors provide a how-to discussion for variable selection and estimate of sample size. However, logistic regression alone can seldom establish causal inference without further steps to explore the often complex relationship amongst variables and outcomes, such as with the use of a directed acyclic graphs. We present key elements that generally should be considered when appraising an article that uses logistic regression. This primer provides a basic understanding of the theory, hands-on construction, model analysis, and limitations of logistic regression in emergency care research.

Logistic regression can provide information about the association of independent variables with important clinical outcomes, which can be the first step to show predictiveness or causation of variables on the outcomes of interest. © 2022 Elsevier Inc.